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28 Einträge gefunden

Up-Net : a generic deep learning-based time stepper for parameterized spatio-temporal dynamics
2023 - TUHH Open Research - frei zugänglich
Data-driven reduced order modeling for mechanical oscillators using Koopman approaches
2023 - TUHH Open Research - frei zugänglich
Towards neural network‐based numerical friction models
2023 - TUHH Open Research - frei zugänglich
bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems
1. Stender, Merten
2. Hoffmann, Norbert
2022 - TUHH Open Research - frei zugänglich
A Novel Approach for the Frequency Shift of a Single Component Eigenmode through Mass Addition in the Context of Brake Squeal Reduction
2022 - TUHH Open Research
Application of machine learning for the generation of tailored wave sequences
2022 - TUHH Open Research
Towards neural network-based numerical friction models
2022 - TUHH Open Research - frei zugänglich
Surface similarity parameter : a new machine learning loss metric for oscillatory spatio-temporal data
2022 - TUHH Open Research - frei zugänglich
Data-driven stability maps for friction induced vibrations
2022 - TUHH Open Research - frei zugänglich
Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube
2021 - TUHH Open Research
Spatially localized vibrations in a rotor subjected to flutter
2021 - TUHH Open Research - frei zugänglich
Explainable machine learning: A case study on impedance tube measurements
2021 - TUHH Open Research
Deep learning for brake squeal: Brake noise detection, characterization and prediction
2021 - TUHH Open Research
Hyperchaos co-existing with periodic orbits in a frictional oscillator
2020 - TUHH Open Research
The basin stability of bi-stable friction-excited oscillators
2020 - TUHH Open Research - frei zugänglich
The extended periodic motion concept for fast limit cycle detection of self-excited systems
2020 - TUHH Open Research
Code: Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube
2020 - TUHH @ Zenodo - frei zugänglich
Deep learning for predicting brake squeal
1. Stender, Merten
2. Hoffmann, Norbert
2020 - TUHH Open Research
Recovery of differential equations from impulse response time series data for model identification and feature extraction
2019 - TUHH Open Research - frei zugänglich
Multi-scale dynamics of particle dampers using wavelets: Extracting particle activity metrics from ring down experiments
2019 - TUHH Open Research

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Up-Net : a generic deep learning-based time stepper for parameterized spatio-temporal dynamics

Data-driven reduced order modeling for mechanical oscillators using Koopman approaches

Towards neural network‐based numerical friction models

bSTAB: an open-source software for computing the basin stability of multi-stable dynamical systems

A Novel Approach for the Frequency Shift of a Single Component Eigenmode through Mass Addition in the Context of Brake Squeal Reduction

Application of machine learning for the generation of tailored wave sequences

Towards neural network-based numerical friction models

Surface similarity parameter : a new machine learning loss metric for oscillatory spatio-temporal data

Data-driven stability maps for friction induced vibrations

Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube

Spatially localized vibrations in a rotor subjected to flutter

Explainable machine learning: A case study on impedance tube measurements

Deep learning for brake squeal: Brake noise detection, characterization and prediction

Hyperchaos co-existing with periodic orbits in a frictional oscillator

The basin stability of bi-stable friction-excited oscillators

The extended periodic motion concept for fast limit cycle detection of self-excited systems

Code: Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube

Deep learning for predicting brake squeal

Recovery of differential equations from impulse response time series data for model identification and feature extraction

Multi-scale dynamics of particle dampers using wavelets: Extracting particle activity metrics from ring down experiments